1. Field of the Invention
This invention generally relates to an audio coding method, and more particularly to a fast bit allocation method for audio coding.
2. Description of Related Art
As the information technology advances, the transmission and storage of audio data are developed toward digitalization. To provide high quality audio transmission and storage, the audio data compression technology is the key technology to the audio data processing. In the traditional audio data compression such as the MPEG-1/2/4 standards and the Dolby AC3 standard, the bit allocation is an important part of the audio data compressor, which controls the compression bit rate and the distortion.
Generally, the input analog audio signal will be sampled to obtain the digitalized audio data. The sampling rate is, for example, 44.1 KHz or 48 KHz. The digital audio data is then divided into the frame data; each frame has 1024 audio samples for example. Then the transformation such as Discrete Cosine Transform (DCT) is applied so that the frame data is transformed from time domain to frequency domain to be the spectral coefficients. The spectral coefficients of each frame will be divided into several bands, which are also called scale factor bands (SFB).
Taking the MPEG-2/4 audio standard as an example, during the compression process, each band has a scale factor (SF) parameter to quantize the spectral coefficients. The SF parameter will affect the quantization error and the noise-to-masking ratio (NMR). The quantized spectral coefficients will be coded according to the Huffman codebook (HCB) parameter selected by each band to achieve the prescribed bit rate. In addition to the coding bits of the spectral coefficients, the differential codes of the SF parameter and the run-length codes of the HCB parameter will also affect the bit rate. The differential codes of the SF parameter and the run-length codes of the HCB parameter for the current band will be affected by the SF parameter and the HCB parameter of the previous band. Hence, it is necessary but very complex to optimize the SF parameter and the HCB parameter to achieve the best possible compression performance with the least compression distortion.
A prior art discloses the joint Trellis-based (JTB) optimization to determine the SF parameter and the HCB parameter simultaneously to minimize average NMR (ANMR) under the prescribed bit rate. See Aggarwal, S. L. Regunathan, K. Rose, “Trellis-based optimization of MPEG-4 advanced audio coding” Proc. IEEE Workshop on Speech Coding, pp. 142-4 2000. In addition, another article also uses JTB optimization to determine the SF parameter and the HCB parameter at the same time. See A. Aggarwal, S. L. Regunathan, K. Rose, “Near-optimal selection of encoding parameter for audio coding” Proc. Of ICASSP, vol. 5, pp. 3269-3272, June 2001. The difference is that, in addition to optimize the average ANMR, the latter also optimizes the maximum NMR (MNMR) under the prescribed bit rate.
Although the above articles can optimize the SF parameter and the HCB parameter at the same time to obtain almost the best compression efficiency, both require a large amount of computation. Hence, they are not suitable for the practical applications that have real-time and/or low-power requirements such as wireless communication systems.